Multi-view Representations for Pose Invariant Face Recognition in Man and Machine

人和机器中姿势不变人脸识别的多视图表示

• 批准号: 2271236
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2271236
• 关键词: Multi; view; Representations; Pose; Invariant

Humans have an incredible ability to identify familiar individuals despite substantial changes in the visual picture. These can include changes in lighting, viewing angle, distance, occluders and peripheral elements such as glasses and hair, and both static and dynamic shape change of the face due to speech and expression. Despite this highly functional human capability, facial appearance variations pose a significant challenge to computer vision systems. Achieving Pose-Invariant Face Recognition (PIFR) in computer vision systems remains a significant stumbling block to realizing the full potential of face recognition as a passive biometric technology. Extensive efforts have been made to try to solve the problem of pose-invariant face recognition in the development Artificial Intelligence, yet it remains a significant barrier. PIFR is achieved effortlessly by the human visual system but at present we do not understand the human system well enough to provide and implement plausible solutions to the clear technological challenges. The aim of this PhD studentship will therefore be to enhance our understanding of how human observers achieve pose invariant recognition of faces in order to inform AI strategies. We will particularly focus on multi-view or pose-aware strategies and compare these against object-based models or pose-agnostic approaches. The work will involve both extensive psychophysical experimentation with human participants and computational experiments exploring computer models of pose-invariant dynamic face recognition. The psychological and psychophysical experimentation will include behavioural studies as well as studies incorporating neuroimaging methods such as functional magnetic resonance imaging (fMRI) to help understand the brain networks involved in pose invariance in humans. These experiments will include determining whether, and how, human observers utilise or disregard the variations in the visual input whilst achieving pose invariant face recognition.

人类有一种令人难以置信的能力来识别熟悉的个体，尽管视觉画面发生了重大变化。这些可以包括照明、视角、距离、遮挡物和外围元素（如眼镜和头发）的变化，以及由于语音和表情而引起的面部的静态和动态形状变化。尽管人类具有这种高度功能性的能力，但面部外观变化对计算机视觉系统构成了重大挑战。在计算机视觉系统中实现姿态不变人脸识别（PIFR）仍然是实现人脸识别作为被动生物识别技术的全部潜力的重要绊脚石。在人工智能的发展过程中，人们已经做了大量的努力试图解决姿态不变的人脸识别问题，但它仍然是一个重大的障碍。PIFR是通过人类视觉系统毫不费力地实现的，但目前我们对人类系统的理解还不够好，无法为明确的技术挑战提供和实施合理的解决方案。因此，这个博士研究生项目的目的是提高我们对人类观察者如何实现人脸姿势不变识别的理解，以便为人工智能策略提供信息。我们将特别关注多视图或姿态感知策略，并将其与基于对象的模型或姿态不可知方法进行比较。这项工作将涉及广泛的心理物理实验与人类参与者和计算实验探索计算机模型的姿态不变的动态人脸识别。心理学和心理物理学实验将包括行为研究以及结合神经成像方法的研究，如功能性磁共振成像（fMRI），以帮助理解人类姿势不变性所涉及的大脑网络。这些实验将包括确定人类观察者是否以及如何利用或忽略视觉输入中的变化，同时实现姿态不变的人脸识别。